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Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying...

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Detalles Bibliográficos
Autores principales: Chen, Xi, Kopsaftopoulos, Fotis, Wu, Qi, Ren, He, Chang, Fu-Kuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982412/
https://www.ncbi.nlm.nih.gov/pubmed/29710832
http://dx.doi.org/10.3390/s18051379
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author Chen, Xi
Kopsaftopoulos, Fotis
Wu, Qi
Ren, He
Chang, Fu-Kuo
author_facet Chen, Xi
Kopsaftopoulos, Fotis
Wu, Qi
Ren, He
Chang, Fu-Kuo
author_sort Chen, Xi
collection PubMed
description In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.
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spelling pubmed-59824122018-06-05 Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches Chen, Xi Kopsaftopoulos, Fotis Wu, Qi Ren, He Chang, Fu-Kuo Sensors (Basel) Article In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles. MDPI 2018-04-29 /pmc/articles/PMC5982412/ /pubmed/29710832 http://dx.doi.org/10.3390/s18051379 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Xi
Kopsaftopoulos, Fotis
Wu, Qi
Ren, He
Chang, Fu-Kuo
Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
title Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
title_full Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
title_fullStr Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
title_full_unstemmed Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
title_short Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches
title_sort flight state identification of a self-sensing wing via an improved feature selection method and machine learning approaches
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982412/
https://www.ncbi.nlm.nih.gov/pubmed/29710832
http://dx.doi.org/10.3390/s18051379
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